We introduce Bi-GNN for modeling biological link prediction tasks such as
drug-drug interaction (DDI) and protein-protein interaction (PPI). Taking
drug-drug interaction as an example, existing methods using machine learning
either only utilize the link structure between drugs without using the graph
representation of each drug molecule, or only leverage the individual drug
compound structures without using graph structure for the higher-level DDI
graph. The key idea of our method is to fundamentally view the data as a
bi-level graph, where the highest level graph represents the interaction
between biological entities (interaction graph), and each biological entity
itself is further expanded to its intrinsic graph representation
(representation graphs), where the graph is either flat like a drug compound or
hierarchical like a protein with amino acid level graph, secondary structure,
tertiary structure, etc. Our model not only allows the usage of information
from both the high-level interaction graph and the low-level representation
graphs, but also offers a baseline for future research opportunities to address
the bi-level nature of the data.

提出了一种名为 Bi-GNN 的方法，用于建模生物学链接预测任务，如药物相互作用（DDI）和蛋白质相互作用（PPI），该方法不仅利用了药物分子本身的图形表示，还包括相互作用图。